Record 03082026 · captured 2026-08-25
The world looked up Spider-Man: Brand New Day. 30 tracked subjects, 25 discussions, 25 papers. This record is frozen and will not change.
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What the most people looked up, ranked by Wikipedia pageviews for that day.
Spider-Man: Brand New Day is a 2026 American superhero film based on the Marvel Comics character Spider-Man. Produced by Columbia Pictures, Marvel Studios, and Pascal Pictures, and distributed by Sony Pictures Releasing, it is the 38th film in the Marvel Cinem
The 2026 SummerSlam, also promoted as SummerSlam: Minnesota, was a professional wrestling pay-per-view (PPV) and livestreaming event produced by WWE. It was the 39th annual SummerSlam and took place as a two-night event on Saturday, August 1, and Sunday, Augus
The Odyssey is a 2026 epic action fantasy film written and directed by Christopher Nolan, who produced it with his wife Emma Thomas. An adaptation of Homer's ancient Greek epic poem the Odyssey, it stars an ensemble cast including Matt Damon, Tom Holland, Anne
Nirmal Purja, also known as Nims or Nimsdai, was a Nepali-British mountaineer. Before pursuing a career in mountaineering, he served in the British Army with the Brigade of Gurkhas and later in the Special Boat Service (SBS), the special forces unit of the Roy
2026 Commonwealth Games medal table
The 2026 Commonwealth Games was a multi-sport event held in Glasgow, Scotland, from 23 July to 2 August 2026. A total of 215 medal events were contested across ten sports.
Vincent Dominic Pastore was an American actor. Often cast as a mafioso, he was perhaps best known for his portrayal of Salvatore "Big Pussy" Bonpensiero on the HBO drama series The Sopranos (1999–2007) earning a Screen Actors Guild Award.
The 2026 Commonwealth Games, officially known as the XXIII Commonwealth Games and commonly known as Glasgow 2026, was a multi-sport event held from 23 July to 2 August 2026 in Glasgow, the largest city in Scotland, for members of the Commonwealth of Nations. T
Thomas Stanley Holland is a British actor. His accolades include a BAFTA Award as well as two Critics' Choice Awards nominations. Holland's films as a leading actor have grossed over $14.9 billion worldwide, making him the Fourth highest-grossing actor of all
Zendaya Maree Stoermer Coleman, known mononymously as Zendaya, is an American actress and singer-songwriter. Known for her work in television and blockbusters, her films as a leading actress have grossed over $9.8 billion worldwide. Her accolades include two P
Pan Am Flight 103 was a regularly scheduled Pan Am flight from Frankfurt to Detroit via stopovers in London and New York City. Shortly after 19:00 GMT on 21 December 1988, the Boeing 747 Clipper Maid of the Seas was destroyed by a bomb while flying over the Sc
.xyz is a top-level domain name that was proposed in ICANN's new generic top-level domain (gTLD) Program for consisting of the last three letters of the Latin-script alphabet. XYZ.com and CentralNic are the registries for the domain, which was created by entre
India at the 2026 Commonwealth Games
India competed at the 2026 Commonwealth Games, held in Glasgow, Scotland, from 23 July to 2 August 2026. It was the country's 19th appearance at the Commonwealth Games, after making its debut at the 1934 Commonwealth Games. The Indian contingent consisted of 1
Tarik Daniel Skubal is an American professional baseball pitcher for the Los Angeles Dodgers of Major League Baseball (MLB). He has previously played in MLB for the Detroit Tigers. Skubal was selected by the Tigers in the ninth round of the 2018 MLB draft and
The Odyssey is one of two major epics of ancient Greek literature attributed to Homer. It is one of the oldest surviving works of literature and remains popular with modern audiences. Like the Iliad, the Odyssey is divided into 24 books. It follows the heroic
The following notable deaths occurred in 2026. Names are reported under the date of death, in alphabetical order. A typical entry reports information in the following sequence:Name, age, country of citizenship at birth, subsequent nationality, what subject was
Ceuta is an autonomous city of Spain on the North African coast. Bordered by Morocco, it lies between the Mediterranean Sea and the Atlantic Ocean. Ceuta is one of the special territories of members of the European Economic Area: it is a part of the Schengen a
Spider-Man: No Way Home is a 2021 American superhero film based on the Marvel Comics character Spider-Man. Produced by Columbia Pictures, Marvel Studios, and Pascal Pictures, and distributed by Sony Pictures Releasing, it is the sequel to Spider-Man: Homecomin
Sadie Elizabeth Sink is an American actress. She began her career in theater as a child, playing the title role in the musical Annie (2012–2014) and young Elizabeth II in the historical play The Audience (2015) on Broadway. In 2016, she made her film debut in
In the early morning hours of November 13, 2022, four University of Idaho students—Madison Mogen, Kaylee Goncalves, Ethan Chapin, and Xana Kernodle—were fatally stabbed in an off-campus house in Moscow, Idaho. On December 30, authorities arrested 28-year-old B
Obsession is a 2025 American supernatural horror film written, directed, and edited by Curry Barker. The film follows Bear, a music store employee who buys a supernatural toy that grants his wish for his friend Nikki to fall in love with him, which makes her b
List of Marvel Cinematic Universe films
The Marvel Cinematic Universe (MCU) centers on American superhero films produced by Marvel Studios, based on characters that appear in publications by Marvel Comics. The MCU is the shared universe in which all of the films are set. Marvel Studios has released
Jonathan Edward Bernthal is an American actor. Known for playing brash, hard-edged, ethically complex characters, he first achieved prominence for his portrayal of Shane Walsh on the AMC horror drama series The Walking Dead (2010–2012). He went on to portray F
Jean Grey is a superhero appearing in American comic books published by Marvel Comics, usually those featuring the X-Men, a group of superheroes of which she is a founding member. Created by writer Stan Lee and artist/co-plotter Jack Kirby, the character first
2026 in film is an overview of events in the film industry scheduled to occur in 2026. Best Picture Academy Award-winners All Quiet on the Western Front and Cimarron entered the public domain this year.
Gatta Kusthi 2 is a 2026 Indian Tamil-language sports comedy drama film written and directed by Chella Ayyavu. It was jointly produced by Ishari K. Ganesh, Vishnu Vishal and Ishan Saksena through the companies Vels Film International, Vishnu Vishal Studioz and
Nicholas Harry Aldis is an English professional wrestler and executive. He is signed to WWE, where he is a producer and the on-screen general manager of SmackDown.
The Devil Wears Prada 2 is a 2026 American comedy drama film directed by David Frankel and written by Aline Brosh McKenna. A sequel to the 2006 film The Devil Wears Prada, it sees Meryl Streep, Anne Hathaway, Emily Blunt, and Stanley Tucci reprising their role
Neatsville is an unincorporated community in Adair County, in the U.S. state of Kentucky. It is located at the junction of Kentucky Route 206 and Kentucky Route 76. Its elevation is 705 feet (215 m). For unknown reasons, the town's name was spelled as Neetsvil
Sir Christopher Edward Nolan is a British and American filmmaker. Known for his Hollywood blockbusters with complex storytelling, Nolan is considered a leading filmmaker of the 21st century. His films have earned over $7.7 billion worldwide, making him the thi
Spider-Man, a superhero character created by Stan Lee and Steve Ditko for American comic books published by Marvel Comics, has appeared in virtually every form of media, including film since the 1970s. CBS's television film pilot for the program The Amazing Sp
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Tensor Data Scattering and the Impossibility of Slicing Theorem
This paper proposes a standard way to represent sparse tensors. A broad theoretical framework for tensor data scattering methods used in various deep learning frameworks is established. This paper presents a theorem that is very important for performance analysis and accelerator optimization for implementing data scattering. The theorem shows how the impossibility of slicing happens in tensor data scattering. A spars
Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error
Inter-beat interval (IBI) measurement enables estimation of heart-rate variability (HRV) which, in turns, can provide early indication of potential cardiovascular diseases. However, extracting IBIs from noisy signals is challenging since the morphology of the signal is distorted in the presence of the noise. Electrocardiogram (ECG) of a person in heavy motion is highly corrupted with noise, known as motion-artifact,
Neural network relief: a pruning algorithm based on neural activity
Current deep neural networks (DNNs) are overparameterized and use most of their neuronal connections during inference for each task. The human brain, however, developed specialized regions for different tasks and performs inference with a small fraction of its neuronal connections. We propose an iterative pruning strategy introducing a simple importance-score metric that deactivates unimportant connections, tackling
SATViz: Real-Time Visualization of Clausal Proofs
Visual layouts of graphs representing SAT instances can highlight the community structure of SAT instances. The community structure of SAT instances has been associated with both instance hardness and known clause quality heuristics. Our tool SATViz visualizes CNF formulas using the variable interaction graph and a force-directed layout algorithm. With SATViz, clause proofs can be animated to continuously highlight v
POSSE-kNN: Pathwise Out-of-Bag Selected Subspace Ensembles for Binary Classification
Nearest neighbour classification is attractive for tabular data, but its performance can deteriorate when a fixed query centred neighbourhood does not follow the local class geometry. This study evaluates POSSE-$k$NN, a pathwise $k$ nearest neighbour ensemble that combines bootstrap sampling, random feature subspaces, out-of-bag (OOB) screening, and selective voting. Within each randomized candidate, pathwise selecti
Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions
We study the tradeoff between consistency and robustness in the context of a single-trajectory time-varying Markov Decision Process (MDP) with untrusted machine-learned advice. Our work departs from the typical approach of treating advice as coming from black-box sources by instead considering a setting where additional information about how the advice is generated is available. We prove a first-of-its-kind consisten
Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces
Tipping points are abrupt, drastic, and often irreversible changes in the evolution of non-stationary and chaotic dynamical systems. For instance, increased greenhouse gas concentrations are predicted to lead to drastic decreases in low cloud cover, referred to as a climatological tipping point. In this paper, we learn the evolution of such non-stationary dynamical systems using a novel recurrent neural operator (RNO
In the mammalian central nervous system, neurons are organized into populations communicating by spike trains propagating along axonal bundles. How such populations encode and transform information is only partially understood. In this study we introduce a mathematical framework derived from a mechanistic model of a single plastic neuron. Within this framework, an algebra of convex cones can rigorously characterize p
This work introduces a novel principle for disentanglement we call mechanism sparsity regularization, which applies when the latent factors of interest depend sparsely on observed auxiliary variables and/or past latent factors. We propose a representation learning method that induces disentanglement by simultaneously learning the latent factors and the sparse causal graphical model that explains them. We develop a no
Gradient-free neural topology optimization: Towards effective fracture-resistant designs
Gradient-free optimizers allow for tackling problems regardless of the smoothness or differentiability of their objective function, but they require many more iterations to converge when compared to gradient-based algorithms. This has made them unviable for topology optimization due to the high computational cost per iteration and the high dimensionality of these problems. We propose a gradient-free neural topology o
Communication-Efficient Secure Aggregation in Decentralized Learning
Decentralized learning (DL) enables participants to collaboratively train models without a central server, yet it faces significant scalability challenges that demand sparsification to reduce the prohibitive communication costs of peer-to-peer exchange. While secure aggregation effectively mitigates privacy risks in standard settings, it has remained fundamentally incompatible with sparsification in decentralized net
Watermarking Language Models with Error Correcting Codes
Recent progress in large language models enables the creation of realistic machine-generated content. Watermarking is a promising approach to distinguish machine-generated text from human text, embedding statistical signals in the output that are ideally undetectable to humans. We propose a watermarking framework that encodes such signals through an error correcting code. Our method, termed robust binary code (RBC) w
Unified continuous-time q-learning for mean-field game and mean-field control problems
This paper studies the continuous-time q-learning in mean-field jump-diffusion models in a setting where the environment simulator does not provide direct access to the population distribution. We propose the integrated q-function in decoupled form (decoupled Iq-function) and establish its martingale characterization, which provides a unified policy evaluation rule for both mean-field game (MFG) and mean-field contro
Contrastive Learning for Image Complexity Representation
Quantifying and evaluating image complexity can be instrumental in enhancing the performance of various computer vision tasks. Supervised learning can effectively learn image complexity features from well-annotated datasets. However, creating such datasets requires expensive manual annotation costs. The models may learn human subjective biases from it. In this work, we introduce the MoCo v2 framework. We utilize cont
Additive manufacturing methods together with topology optimization have enabled the creation of multiscale structures with controlled spatially-varying material microstructure. However, topology optimization or inverse design of such structures in the presence of nonlinearities remains a challenge due to the expense of computational homogenization methods and the complexity of differentiably parameterizing the micros
SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction
Material discovery is a cornerstone of modern science, driving advancements in diverse disciplines from biomedical technology to climate solutions. Predicting synthesizability, a critical factor in realizing novel materials, remains a complex challenge due to the limitations of traditional heuristics and thermodynamic proxies. While stability metrics such as formation energy offer partial insights, they fail to accou
Nonlinear Assimilation via Score-based Sequential Langevin Sampling
This paper introduces score-based sequential Langevin sampling (SSLS), a novel approach to nonlinear data assimilation within a recursive Bayesian filtering framework. The proposed method decomposes the assimilation process into alternating prediction and update steps, using dynamic models for state prediction and incorporating observational data via score-based Langevin Monte Carlo during the updates. To overcome in
Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook
We survey deepfake generation and detection techniques, covering all deepfake media types: image, video, audio and multimodal content. We identify various kinds of deepfakes and construct taxonomies of deepfake generation and detection methods, illustrating the important groups of methods. Next, we gather datasets used for deepfake detection and provide updated rankings of the best performing detectors on the most po
Dual-Force: Enhanced Offline Diversity Maximization under Imitation Constraints
Offline diversity maximization under imitation constraints can transform demonstration data into a set of distinct behavioral policies, improving robustness to distribution shift without additional environment interaction. In practice, however, existing offline approaches often rely on mutual-information objectives that require training a skill discriminator and can become unstable under the non-stationary rewards in
Self-reflecting Large Language Models: A Hegelian Dialectical Approach
In this paper, we introduce a self-reflection framework for Large Language Models (LLMs) grounded in the Hegelian Dialectic, a philosophical method in which an initial proposition is challenged by a generated opposition, and both are reconciled into a unified, more comprehensive idea. We formalize this process as an iterative operator over the space of consistent theories and apply it to two complementary tasks:(i) g
We present PLANTOR, a framework for generating and executing multi-robot task plans from natural-language task descriptions through LLM-assisted knowledge-base construction. The approach uses large language models to synthesize a structured Prolog knowledge-base, applies consistency checks to detect and repair modeling errors, generates a high-level symbolic plan, refines it into low-level robot actions, and computes
Dimensionality reduction for homological stability and global structure preservation
We propose DiRe, a force-directed dimensionality reduction framework designed to preserve global structure and homological features while remaining practical on modern hardware. The method combines an initial embedding with a graph-based layout optimization and evaluates the resulting low-dimensional representation using local distortion, context preservation, and persistent homology measures. Across the benchmark su
Reproducing Human Individual Motor Signatures: A Data-Driven Approach for Repetitive Motion
The deployment of autonomous virtual avatars (in extended reality) and robots in human group activities---such as rehabilitation therapy, sports, and manufacturing---is expected to increase as these technologies become more pervasive. Designing cognitive architectures and control strategies to drive these agents requires realistic models of human motion. Furthermore, recent research has shown that each person exhibit
Concept Extraction for Time Series with ECLAD-ts
Convolutional neural networks (CNNs) for time series classification (TSC) are being increasingly used in applications ranging from quality prediction to medical diagnosis. The black box nature of these models makes understanding their prediction process difficult. This issue is crucial because CNNs are prone to learning shortcuts and biases, compromising their robustness and alignment with human expectations. To asse
Estimating Item Difficulty Using Large Language Models and Tree-Based Machine Learning Algorithms
Estimating item difficulty through field-testing is often resource-intensive and time-consuming. As such, there is strong motivation to develop methods that can predict item difficulty at scale using only the item content. Large Language Models (LLMs) represent a new frontier for this goal. The present research examines the feasibility of using an LLM to predict item difficulty for K-5 mathematics and reading assessm
Notable events recorded on this day and month across all years.